Lesion Segmentation of MS
Terminology: Neuroimaging to Data/Statistics
- Segmentation ⇔ classification
- Image ⇔ 3-dimensional array
- Mask/Region of Interest ⇔ binary (0/1) image
- Registration ⇔ Spatial Normalization/Standarization
- “Lining up” Brains
Public Dataset with Lesion Segmentation
- “A novel public MR image dataset of multiple sclerosis patients with lesion segmentations based on multi-rater consensus” (Lesjak et al. 2018)
- Data Published at http://lit.fe.uni-lj.si/tools.php?lang=eng
- 30 subjects with MRI (3T Siemens Trio)
- Manually segmented by 3 expert raters
- Creative-Commons Attribution (CC-BY)
- All analysis and data located: https://github.com/muschellij2/open_ms_data
Demographic Data
- On many different therapies (9 no therapy), age IQR: 33 - 42, EDSS IQR: 1.5 - 4
| Variable | Overall |
|---|---|
| n | 30 |
| Age (mean (sd)) | 39.27 (10.12) |
| sex = M (%) | 7 (23.3) |
| EDSS (mean (sd)) | 2.61 (1.88) |
| Lesion_Volume (mean (sd)) | 17.40 (16.13) |
| MS_Subtype (%) | |
| Clinically Isolated Syndrome | 2 (6.7) |
| Progressive-relapsing | 1 (3.3) |
| Relapsing-remitting | 24 (80.0) |
| Secondary-progressive | 2 (6.7) |
| Unspecified | 1 (3.3) |
Imaging Data
- 2D T1 (TR=2000ms, TE=20ms, TI=800ms) and after gadolinium
- 2D T2 (TR=6000ms, TE=120ms), 3D FLAIR (TR=5000ms, TE=392ms, TI=1800 ms)
- Fluid attenuated inversion recovery - reduce signal of fluids
- All had flip angle of 120\(^{\circ}\)
Project Goal 
Image Representation: voxels (3D pixels)
Step 1: Image Processing: Workflow
The N4 (Tustison et al. 2010) EM-style model assumed is: \[ \log(x(v)) = \log(u(v)) + \log( f(v) ) \]
- \(x\): given image
- \(u\): uncorrupted image
- \(f\): bias field
- \(v\): location in the image
Step 1: Image Processing: MALF
Figure from Multi-Atlas Skull Stripping method paper (Doshi et al. 2013):
- Register templates to an image using the T1 for that subject
- Apply transformation to the label/mask
- Average each voxel over all templates
- there are “smarter” (e.g. weighted) ways
Step 2: Create Predictors for each Sequence
- Predictors created with intensity-normalized data
- Quantile images, smoothers, local moments
- Tissue class probability with local moments: MALF and FAST (Zhang, Brady, and Smith 2001)
- Z-score to a population template
A package to do all this: smri.process
- GitHub package (
muschellij2/smri.process)
Data Structure for One Patient
Step 3: Aggregate Data
Training Data Structure
- Sample
10%of the voxels (save computation time) - Stack together 14 randomly selected patients, stratified by age (over median) and volume
- Train model/classifier on this design matrix
- Smooth the probability map
- Test on 16 hold out
Step 4: Fit Models / Classifier
Let \(y_{i}(v)\) be the presence / absence of lesion for voxel \(v\) from person \(i\).
General model form: \[
P(Y_{i}(v) = 1) \propto f(X_{i}(v))
\]
- Previous work - OASIS (Sweeney et al. 2013):
\[ f(X_{i}(v)) = \text{expit} \left\{ \beta_0 + \sum_{k} x_{k}(v)\beta_{k} + x_{k}(v) \times x_{10, k} \beta_{10,k} + x_{k}(v) \times x_{20, k} \beta_{20,k}\right\} \]
\(k \in \{T1, T2, FLAIR, PD\}\).
- With the original model w/o T1Post and a re-trained model
Models Fit on the Training Data
- \(85\) predictors were generated
- Random Forests (Wright and Ziegler 2017), (Breiman 2001)
- With 5 fold cross-validation, default 500 trees, mtry: \(\sqrt{p}\)
- With and without the T1-Post for comparison to OASIS
\(f(X_{i}(v)) \propto\)
For each model (RF with and w/o T1Post and OASIS retrained or not)
- Estimate a probability cutoff on training data
- Predict on test data, assess performance acrosss all voxels in the brain
Assessing Performance
For each test scan, and over all test scans, we can calculate the following 2-by-2 table, where cells represent number of voxels and corresponding Venn diagram:
Manual 0 1 Auto 0 TN FN 1 FP TP
Dice Coeffiicent (Dice 1945): \[ \text{Dice} = \frac{2\times\text{TP}}{2\times\text{TP} + \text{FN} + {FP}} \]
Dice Results (Triangle is population Dice)
Patient with Median DSI (0.63) in Test
Patient with High DSI (0.73) in Test
- Top predictors in RF model
- T1Post not in there
- Tissue segmentations are important predictors
- FLAIR as well
RF Predicted Volume Estimates True Volume
OASIS: not so much
Brain Stem Lesions Estimated
Conclusions of Lesion Analyses
- We can segment MS lesions reasonably well
- Better models with larger samples
- Needs to be more stable/accurate for a biomarker
- Location may also be relevant and not taken into account
- Is the brain stem an area we should focus on or remove from assessment?
Next Steps/Questions
- Run new processing the 131 patients from OASIS paper
- Gray matter injury estimation
- Is EDSS the clinical score we should be correlating with?
- “Black hole” lesions using the T1-post image, these may show “active” lesions
Thank You
Breiman, Leo. 2001. “Random Forests.” Machine Learning 45 (1). Springer:5–32.
Dice, Lee R. 1945. “Measures of the Amount of Ecologic Association Between Species.” Ecology 26 (3):297–302. http://www.jstor.org/stable/1932409.
Doshi, Jimit, Guray Erus, Yangming Ou, Bilwaj Gaonkar, and Christos Davatzikos. 2013. “Multi-Atlas Skull-Stripping.” Academic Radiology 20 (12). Elsevier:1566–76.
Lesjak, Žiga, Alfiia Galimzianova, Aleš Koren, Matej Lukin, Franjo Pernuš, Boštjan Likar, and Žiga Špiclin. 2018. “A Novel Public MR Image Dataset of Multiple Sclerosis Patients with Lesion Segmentations Based on Multi-Rater Consensus.” Neuroinformatics 16 (1). Springer:51–63.
Sweeney, Elizabeth M, Russell T Shinohara, Navid Shiee, Farrah J Mateen, Avni A Chudgar, Jennifer L Cuzzocreo, Peter A Calabresi, Dzung L Pham, Daniel S Reich, and Ciprian M Crainiceanu. 2013. “OASIS Is Automated Statistical Inference for Segmentation, with Applications to Multiple Sclerosis Lesion Segmentation in MRI.” NeuroImage: Clinical 2. Elsevier:402–13.
Tustison, Nicholas J., Brian B. Avants, Philip A. Cook, Yuanjie Zheng, Alexander Egan, Paul A. Yushkevich, and James C. Gee. 2010. “N4ITK: Improved N3 Bias Correction.” IEEE Transactions on Medical Imaging 29 (6):1310–20. https://doi.org/10.1109/TMI.2010.2046908.
Wright, Marvin N., and Andreas Ziegler. 2017. “ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R.” Journal of Statistical Software 77 (1):1–17. https://doi.org/10.18637/jss.v077.i01.
Zhang, Yongyue, Michael Brady, and Stephen Smith. 2001. “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm.” Medical Imaging, IEEE Transactions on 20 (1):45–57. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=906424.